Biosignal Compression Toolbox for Digital Biomarker Discovery
A critical challenge to using longitudinal wearable sensor biosignal data for healthcare applications and digital biomarker development is the exacerbation of the healthcare “data deluge,” leading to new data storage and organization challenges and costs. Data aggregation, sampling rate minimization...
Uložené v:
| Vydané v: | Sensors (Basel, Switzerland) Ročník 21; číslo 2; s. 516 |
|---|---|
| Hlavní autori: | , , , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
Switzerland
MDPI
13.01.2021
MDPI AG |
| Predmet: | |
| ISSN: | 1424-8220, 1424-8220 |
| On-line prístup: | Získať plný text |
| Tagy: |
Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
|
| Abstract | A critical challenge to using longitudinal wearable sensor biosignal data for healthcare applications and digital biomarker development is the exacerbation of the healthcare “data deluge,” leading to new data storage and organization challenges and costs. Data aggregation, sampling rate minimization, and effective data compression are all methods for consolidating wearable sensor data to reduce data volumes. There has been limited research on appropriate, effective, and efficient data compression methods for biosignal data. Here, we examine the application of different data compression pipelines built using combinations of algorithmic- and encoding-based methods to biosignal data from wearable sensors and explore how these implementations affect data recoverability and storage footprint. Algorithmic methods tested include singular value decomposition, the discrete cosine transform, and the biorthogonal discrete wavelet transform. Encoding methods tested include run-length encoding and Huffman encoding. We apply these methods to common wearable sensor data, including electrocardiogram (ECG), photoplethysmography (PPG), accelerometry, electrodermal activity (EDA), and skin temperature measurements. Of the methods examined in this study and in line with the characteristics of the different data types, we recommend direct data compression with Huffman encoding for ECG, and PPG, singular value decomposition with Huffman encoding for EDA and accelerometry, and the biorthogonal discrete wavelet transform with Huffman encoding for skin temperature to maximize data recoverability after compression. We also report the best methods for maximizing the compression ratio. Finally, we develop and document open-source code and data for each compression method tested here, which can be accessed through the Digital Biomarker Discovery Pipeline as the “Biosignal Data Compression Toolbox,” an open-source, accessible software platform for compressing biosignal data. |
|---|---|
| AbstractList | A critical challenge to using longitudinal wearable sensor biosignal data for healthcare applications and digital biomarker development is the exacerbation of the healthcare "data deluge," leading to new data storage and organization challenges and costs. Data aggregation, sampling rate minimization, and effective data compression are all methods for consolidating wearable sensor data to reduce data volumes. There has been limited research on appropriate, effective, and efficient data compression methods for biosignal data. Here, we examine the application of different data compression pipelines built using combinations of algorithmic- and encoding-based methods to biosignal data from wearable sensors and explore how these implementations affect data recoverability and storage footprint. Algorithmic methods tested include singular value decomposition, the discrete cosine transform, and the biorthogonal discrete wavelet transform. Encoding methods tested include run-length encoding and Huffman encoding. We apply these methods to common wearable sensor data, including electrocardiogram (ECG), photoplethysmography (PPG), accelerometry, electrodermal activity (EDA), and skin temperature measurements. Of the methods examined in this study and in line with the characteristics of the different data types, we recommend direct data compression with Huffman encoding for ECG, and PPG, singular value decomposition with Huffman encoding for EDA and accelerometry, and the biorthogonal discrete wavelet transform with Huffman encoding for skin temperature to maximize data recoverability after compression. We also report the best methods for maximizing the compression ratio. Finally, we develop and document open-source code and data for each compression method tested here, which can be accessed through the Digital Biomarker Discovery Pipeline as the "Biosignal Data Compression Toolbox," an open-source, accessible software platform for compressing biosignal data. A critical challenge to using longitudinal wearable sensor biosignal data for healthcare applications and digital biomarker development is the exacerbation of the healthcare "data deluge," leading to new data storage and organization challenges and costs. Data aggregation, sampling rate minimization, and effective data compression are all methods for consolidating wearable sensor data to reduce data volumes. There has been limited research on appropriate, effective, and efficient data compression methods for biosignal data. Here, we examine the application of different data compression pipelines built using combinations of algorithmic- and encoding-based methods to biosignal data from wearable sensors and explore how these implementations affect data recoverability and storage footprint. Algorithmic methods tested include singular value decomposition, the discrete cosine transform, and the biorthogonal discrete wavelet transform. Encoding methods tested include run-length encoding and Huffman encoding. We apply these methods to common wearable sensor data, including electrocardiogram (ECG), photoplethysmography (PPG), accelerometry, electrodermal activity (EDA), and skin temperature measurements. Of the methods examined in this study and in line with the characteristics of the different data types, we recommend direct data compression with Huffman encoding for ECG, and PPG, singular value decomposition with Huffman encoding for EDA and accelerometry, and the biorthogonal discrete wavelet transform with Huffman encoding for skin temperature to maximize data recoverability after compression. We also report the best methods for maximizing the compression ratio. Finally, we develop and document open-source code and data for each compression method tested here, which can be accessed through the Digital Biomarker Discovery Pipeline as the "Biosignal Data Compression Toolbox," an open-source, accessible software platform for compressing biosignal data.A critical challenge to using longitudinal wearable sensor biosignal data for healthcare applications and digital biomarker development is the exacerbation of the healthcare "data deluge," leading to new data storage and organization challenges and costs. Data aggregation, sampling rate minimization, and effective data compression are all methods for consolidating wearable sensor data to reduce data volumes. There has been limited research on appropriate, effective, and efficient data compression methods for biosignal data. Here, we examine the application of different data compression pipelines built using combinations of algorithmic- and encoding-based methods to biosignal data from wearable sensors and explore how these implementations affect data recoverability and storage footprint. Algorithmic methods tested include singular value decomposition, the discrete cosine transform, and the biorthogonal discrete wavelet transform. Encoding methods tested include run-length encoding and Huffman encoding. We apply these methods to common wearable sensor data, including electrocardiogram (ECG), photoplethysmography (PPG), accelerometry, electrodermal activity (EDA), and skin temperature measurements. Of the methods examined in this study and in line with the characteristics of the different data types, we recommend direct data compression with Huffman encoding for ECG, and PPG, singular value decomposition with Huffman encoding for EDA and accelerometry, and the biorthogonal discrete wavelet transform with Huffman encoding for skin temperature to maximize data recoverability after compression. We also report the best methods for maximizing the compression ratio. Finally, we develop and document open-source code and data for each compression method tested here, which can be accessed through the Digital Biomarker Discovery Pipeline as the "Biosignal Data Compression Toolbox," an open-source, accessible software platform for compressing biosignal data. |
| Author | Bent, Brinnae Dunn, Jessilyn P. Lu, Baiying Kim, Juseong |
| AuthorAffiliation | 2 Department of Biostatistics and Bioinformatics, Duke University, Durham, NC 27708, USA 1 Department of Biomedical Engineering, Duke University, Durham, NC 27708, USA; brinnae.bent@duke.edu (B.B.); baiying.lu@duke.edu (B.L.); juseong.kim@duke.edu (J.K.) |
| AuthorAffiliation_xml | – name: 2 Department of Biostatistics and Bioinformatics, Duke University, Durham, NC 27708, USA – name: 1 Department of Biomedical Engineering, Duke University, Durham, NC 27708, USA; brinnae.bent@duke.edu (B.B.); baiying.lu@duke.edu (B.L.); juseong.kim@duke.edu (J.K.) |
| Author_xml | – sequence: 1 givenname: Brinnae orcidid: 0000-0002-7039-0177 surname: Bent fullname: Bent, Brinnae – sequence: 2 givenname: Baiying orcidid: 0000-0002-6345-235X surname: Lu fullname: Lu, Baiying – sequence: 3 givenname: Juseong orcidid: 0000-0002-0576-5956 surname: Kim fullname: Kim, Juseong – sequence: 4 givenname: Jessilyn P. orcidid: 0000-0002-3241-8183 surname: Dunn fullname: Dunn, Jessilyn P. |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/33450898$$D View this record in MEDLINE/PubMed |
| BookMark | eNptkTlPAzEQhS0E4ggU_AGUEoqAz41dgAThlJBo0luzPoJhsw72JoJ_jyEQAaKyNfP5vfG8HbTextYhtE_wMWMKn2RKMMWCVGtom3DKB5JSvP7jvoV2cn7CmDLG5CbaYowLLJXcRqcXIeYwaaHpj-J0llzOIbb9cYxNHV_7Pqb-ZZiErvQLOYX07D4q2cSFS2-7aMNDk93e19lD4-ur8eh2cP9wczc6vx8Yzkk3ACK9UNR6K6wjaii9sYxXrJZGUlIprpw11HIPmFsCILnFmLAhGGwYcNZDd0tZG-FJz1Ioc7zpCEF_FmKaaEhdMI3TikkragNMVpSDsBKYEpxQ4LVXvvy_h86WWrN5PS22ru0SNL9Ef3fa8KgncaGHksqy7SJw-CWQ4svc5U5Pyzpc00Dr4jxryodSyIqJqqAHP71WJt_rL8DREjAp5pycXyEE649o9Srawp78YU2JpStplTFD88-Ld-HVpPU |
| CitedBy_id | crossref_primary_10_1007_s11227_022_04535_y crossref_primary_10_3389_fpsyt_2021_740292 crossref_primary_10_3390_sym14061139 crossref_primary_10_1146_annurev_bioeng_103020_040136 |
| Cites_doi | 10.1007/s11277-019-06513-9 10.1016/j.measurement.2017.11.006 10.1038/s41746-019-0217-7 10.3390/app10175842 10.1186/2047-2501-2-3 10.1016/j.compbiomed.2017.05.024 10.1016/j.bspc.2018.06.009 10.1117/12.2299967 10.1017/cts.2020.511 10.3390/s19112450 10.1109/TCAD.2003.811452 10.2217/pme-2018-0044 10.9790/0661-1161519 10.1111/j.1467-8659.2008.01309.x 10.1109/TNSRE.2018.2826559 10.3390/s19163445 10.1109/TMC.2010.264 10.1002/mp.13886 10.1007/s10470-018-1323-1 10.1109/10.730435 10.1038/s41746-020-0226-6 10.1017/cts.2020.526 10.1016/j.medengphy.2005.02.007 10.1109/TBME.2018.2883396 10.1088/1361-6579/aa5efa 10.1109/TBME.2011.2156794 10.1016/j.measurement.2018.10.061 |
| ContentType | Journal Article |
| Copyright | 2021 by the authors. 2021 |
| Copyright_xml | – notice: 2021 by the authors. 2021 |
| DBID | AAYXX CITATION CGR CUY CVF ECM EIF NPM 7X8 5PM DOA |
| DOI | 10.3390/s21020516 |
| DatabaseName | CrossRef Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed MEDLINE - Academic PubMed Central (Full Participant titles) DOAJ Directory of Open Access Journals |
| DatabaseTitle | CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) MEDLINE - Academic |
| DatabaseTitleList | MEDLINE MEDLINE - Academic CrossRef |
| Database_xml | – sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 3 dbid: 7X8 name: MEDLINE - Academic url: https://search.proquest.com/medline sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering |
| EISSN | 1424-8220 |
| ExternalDocumentID | oai_doaj_org_article_938d5bca38624a5d8a395412a4bf9f23 PMC7828339 33450898 10_3390_s21020516 |
| Genre | Letter Correspondence |
| GrantInformation_xml | – fundername: Chan Zuckerberg Initiative grantid: 2020-218599 |
| GroupedDBID | --- 123 2WC 53G 5VS 7X7 88E 8FE 8FG 8FI 8FJ AADQD AAHBH AAYXX ABDBF ABUWG ACUHS ADBBV ADMLS AENEX AFFHD AFKRA AFZYC ALMA_UNASSIGNED_HOLDINGS BENPR BPHCQ BVXVI CCPQU CITATION CS3 D1I DU5 E3Z EBD ESX F5P FYUFA GROUPED_DOAJ GX1 HH5 HMCUK HYE IAO ITC KQ8 L6V M1P M48 MODMG M~E OK1 OVT P2P P62 PHGZM PHGZT PIMPY PJZUB PPXIY PQQKQ PROAC PSQYO RNS RPM TUS UKHRP XSB ~8M ALIPV CGR CUY CVF ECM EIF NPM 7X8 5PM |
| ID | FETCH-LOGICAL-c441t-a18f592dfd5de1978fcd3463b8c8216949edc2d4fa04d1aa84d00137ac0c3a43 |
| IEDL.DBID | DOA |
| ISICitedReferencesCount | 5 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000611694000001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 1424-8220 |
| IngestDate | Fri Oct 03 12:44:15 EDT 2025 Tue Nov 04 01:48:02 EST 2025 Sun Nov 09 13:05:35 EST 2025 Thu Apr 03 07:06:47 EDT 2025 Tue Nov 18 21:57:45 EST 2025 Sat Nov 29 07:15:42 EST 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 2 |
| Keywords | accelerometry data compression electrocardiogram data photoplethysmography biosignal electrodermal activity wearables data compression algorithms |
| Language | English |
| License | Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c441t-a18f592dfd5de1978fcd3463b8c8216949edc2d4fa04d1aa84d00137ac0c3a43 |
| Notes | content type line 23 SourceType-Scholarly Journals-1 ObjectType-Correspondence-1 |
| ORCID | 0000-0002-7039-0177 0000-0002-6345-235X 0000-0002-3241-8183 0000-0002-0576-5956 |
| OpenAccessLink | https://doaj.org/article/938d5bca38624a5d8a395412a4bf9f23 |
| PMID | 33450898 |
| PQID | 2478586356 |
| PQPubID | 23479 |
| ParticipantIDs | doaj_primary_oai_doaj_org_article_938d5bca38624a5d8a395412a4bf9f23 pubmedcentral_primary_oai_pubmedcentral_nih_gov_7828339 proquest_miscellaneous_2478586356 pubmed_primary_33450898 crossref_primary_10_3390_s21020516 crossref_citationtrail_10_3390_s21020516 |
| PublicationCentury | 2000 |
| PublicationDate | 20210113 |
| PublicationDateYYYYMMDD | 2021-01-13 |
| PublicationDate_xml | – month: 1 year: 2021 text: 20210113 day: 13 |
| PublicationDecade | 2020 |
| PublicationPlace | Switzerland |
| PublicationPlace_xml | – name: Switzerland |
| PublicationTitle | Sensors (Basel, Switzerland) |
| PublicationTitleAlternate | Sensors (Basel) |
| PublicationYear | 2021 |
| Publisher | MDPI MDPI AG |
| Publisher_xml | – name: MDPI – name: MDPI AG |
| References | Mueller (ref_11) 1978; Volume 14 Hejrati (ref_31) 2017; 87 Sadhukhan (ref_20) 2019; 134 Choi (ref_12) 2017; 38 Mukhopadhyay (ref_19) 2019; 66 Rajankar (ref_28) 2019; 98 ref_36 Mahadevan (ref_6) 2020; 3 Wu (ref_21) 2011; 10 ref_35 ref_34 Dhar (ref_18) 2018; 116 ref_33 ref_10 Jas (ref_29) 2003; 22 Schueler (ref_8) 2019; 6 Dunn (ref_9) 2018; 15 (ref_32) 2005; 27 Raghupathi (ref_4) 2014; 2 ref_15 ref_37 Shaw (ref_24) 2018; 26 Arefan (ref_7) 2020; 47 Jha (ref_25) 2018; 46 Lee (ref_17) 2011; 58 ref_23 Gu (ref_22) 2009; 28 ref_1 ref_3 ref_2 Chen (ref_16) 1998; 45 Bej (ref_13) 2013; 11 ref_26 (ref_14) 2019; 9 ref_5 (ref_27) 2019; 108 Bent (ref_30) 2020; 3 |
| References_xml | – ident: ref_3 – volume: 108 start-page: 2137 year: 2019 ident: ref_27 article-title: An Efficient Algorithm Based on Combined Encoding Techniques for Compression of ECG Data from Multiple Leads publication-title: Wirel. Pers. Commun. doi: 10.1007/s11277-019-06513-9 – volume: 116 start-page: 533 year: 2018 ident: ref_18 article-title: An efficient data compression and encryption technique for PPG signal publication-title: Meas. J. Int. Meas. Confed. doi: 10.1016/j.measurement.2017.11.006 – ident: ref_26 – ident: ref_34 – volume: 3 start-page: 5 year: 2020 ident: ref_6 article-title: Development of digital biomarkers for resting tremor and bradykinesia using a wrist-worn wearable device publication-title: NPJ Digit. Med. doi: 10.1038/s41746-019-0217-7 – ident: ref_35 doi: 10.3390/app10175842 – volume: 2 start-page: 3 year: 2014 ident: ref_4 article-title: Big data analytics in healthcare: Promise and potential publication-title: Health Inf. Sci. Syst. doi: 10.1186/2047-2501-2-3 – volume: 87 start-page: 87 year: 2017 ident: ref_31 article-title: A new near-lossless EEG compression method using ANN-based reconstruction technique publication-title: Comput. Biol. Med. doi: 10.1016/j.compbiomed.2017.05.024 – volume: 46 start-page: 174 year: 2018 ident: ref_25 article-title: Electrocardiogram data compression using DCT based discrete orthogonal Stockwell transform publication-title: Biomed. Signal Process. Control doi: 10.1016/j.bspc.2018.06.009 – ident: ref_15 doi: 10.1117/12.2299967 – ident: ref_37 – volume: Volume 14 start-page: 81 year: 1978 ident: ref_11 article-title: Arrhythmia Detection Program for an Ambulatory Ecg Monitor publication-title: Proceedings of the Biomedical Sciences Instrumentation – ident: ref_1 – ident: ref_10 doi: 10.1017/cts.2020.511 – ident: ref_23 doi: 10.3390/s19112450 – volume: 22 start-page: 797 year: 2003 ident: ref_29 article-title: An efficient test vector compression scheme using selective huffman coding publication-title: IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. doi: 10.1109/TCAD.2003.811452 – volume: 15 start-page: 429 year: 2018 ident: ref_9 article-title: Wearables and the medical revolution publication-title: Pers. Med. doi: 10.2217/pme-2018-0044 – volume: 11 start-page: 15 year: 2013 ident: ref_13 article-title: Comparison Study of Lossless Data Compression Algorithms for Text Data publication-title: IOSR-JCE doi: 10.9790/0661-1161519 – volume: 28 start-page: 1 year: 2009 ident: ref_22 article-title: Compression of Human Motion Capture Data Using Motion Pattern Indexing publication-title: Comput. Graph. Forum doi: 10.1111/j.1467-8659.2008.01309.x – volume: 9 start-page: 1 year: 2019 ident: ref_14 article-title: Effective high compression of ECG signals at low level distortion publication-title: Sci. Rep. – volume: 26 start-page: 957 year: 2018 ident: ref_24 article-title: Highly efficient compression algorithms for multichannel EEG publication-title: IEEE Trans. Neural Syst. Rehabil. Eng. doi: 10.1109/TNSRE.2018.2826559 – ident: ref_33 – ident: ref_36 doi: 10.3390/s19163445 – ident: ref_2 – volume: 10 start-page: 1459 year: 2011 ident: ref_21 article-title: Data compression by temporal and spatial correlations in a body-area sensor network: A case study in pilates motion recognition publication-title: IEEE Trans. Mob. Comput. doi: 10.1109/TMC.2010.264 – volume: 47 start-page: 110 year: 2020 ident: ref_7 article-title: Deep learning modeling using normal mammograms for predicting breast cancer risk publication-title: Med. Phys. doi: 10.1002/mp.13886 – volume: 98 start-page: 59 year: 2019 ident: ref_28 article-title: An electrocardiogram signal compression techniques: A comprehensive review publication-title: Analog Integr. Circuits Signal Process. doi: 10.1007/s10470-018-1323-1 – volume: 45 start-page: 1414 year: 1998 ident: ref_16 article-title: A wavelet transform-based ECG compression method guaranteeing desired signal quality publication-title: IEEE Trans. Biomed. Eng. doi: 10.1109/10.730435 – volume: 3 start-page: 18 year: 2020 ident: ref_30 article-title: Investigating sources of inaccuracy in wearable optical heart rate sensors publication-title: NPJ Digit. Med. doi: 10.1038/s41746-020-0226-6 – ident: ref_5 doi: 10.1017/cts.2020.526 – volume: 27 start-page: 798 year: 2005 ident: ref_32 article-title: On the use of PRD and CR parameters for ECG compression publication-title: Med. Eng. Phys. doi: 10.1016/j.medengphy.2005.02.007 – volume: 6 start-page: 217 year: 2019 ident: ref_8 article-title: Editorial: Can Digital Technology Advance the Development of Treatments for Alzheimer’s Disease? publication-title: J. Prev. Alzheimer’s Dis. – volume: 66 start-page: 2081 year: 2019 ident: ref_19 article-title: Compression of Steganographed PPG Signal with Guaranteed Reconstruction Quality Based on Optimum Truncation of Singular Values and ASCII Character Encoding publication-title: IEEE Trans. Biomed. Eng. doi: 10.1109/TBME.2018.2883396 – volume: 38 start-page: 586 year: 2017 ident: ref_12 article-title: Photoplethysmography sampling frequency: Pilot assessment of how low can we go to analyze pulse rate variability with reliability? publication-title: Physiol. Meas. doi: 10.1088/1361-6579/aa5efa – volume: 58 start-page: 2448 year: 2011 ident: ref_17 article-title: A real-time ECG data compression and transmission algorithm for an e-health device publication-title: IEEE Trans. Biomed. Eng. doi: 10.1109/TBME.2011.2156794 – volume: 134 start-page: 153 year: 2019 ident: ref_20 article-title: Adaptive Band Limit Estimation based PPG data compression for portable home monitors publication-title: Meas. J. Int. Meas. Confed. doi: 10.1016/j.measurement.2018.10.061 |
| SSID | ssj0023338 |
| Score | 2.3665714 |
| Snippet | A critical challenge to using longitudinal wearable sensor biosignal data for healthcare applications and digital biomarker development is the exacerbation of... |
| SourceID | doaj pubmedcentral proquest pubmed crossref |
| SourceType | Open Website Open Access Repository Aggregation Database Index Database Enrichment Source |
| StartPage | 516 |
| SubjectTerms | Algorithms biosignal data Data Compression data compression algorithms electrocardiogram Electrocardiography Letter Photoplethysmography Signal Processing, Computer-Assisted Wavelet Analysis wearables |
| Title | Biosignal Compression Toolbox for Digital Biomarker Discovery |
| URI | https://www.ncbi.nlm.nih.gov/pubmed/33450898 https://www.proquest.com/docview/2478586356 https://pubmed.ncbi.nlm.nih.gov/PMC7828339 https://doaj.org/article/938d5bca38624a5d8a395412a4bf9f23 |
| Volume | 21 |
| WOSCitedRecordID | wos000611694000001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVAON databaseName: DOAJ Directory of Open Access Journals customDbUrl: eissn: 1424-8220 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0023338 issn: 1424-8220 databaseCode: DOA dateStart: 20010101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 1424-8220 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0023338 issn: 1424-8220 databaseCode: M~E dateStart: 20010101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre – providerCode: PRVPQU databaseName: Health & Medical Collection customDbUrl: eissn: 1424-8220 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0023338 issn: 1424-8220 databaseCode: 7X7 dateStart: 20010101 isFulltext: true titleUrlDefault: https://search.proquest.com/healthcomplete providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 1424-8220 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0023338 issn: 1424-8220 databaseCode: BENPR dateStart: 20010101 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: Publicly Available Content Database customDbUrl: eissn: 1424-8220 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0023338 issn: 1424-8220 databaseCode: PIMPY dateStart: 20010101 isFulltext: true titleUrlDefault: http://search.proquest.com/publiccontent providerName: ProQuest |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1Nb9QwEB1B4UAPFd9NgVVAHLhETTxObB8p3QokulqhPSynyLHjslJJUHeL4MJvZybJrnarSly45OCMlMmMLc9zXt4AvPUoq1qkdRIkykTKIBODNiTK14iFCspWnWT-ZzWZ6PncTLdafTEnrJcH7gN3bFD7vHIW-U8Gm3tt0eQyE1ZWwQTR6XymyqzB1AC1kJBXryOEBOqPlwxsaPoVO7tPJ9J_W2V5kyC5teOcPYSDoVSM3_cuPoI7dfMY9rcEBJ8An9EyA4PMeGH3nNYmnrXtZdX-iqkgjU8XF9wXJCbL70zF4ZGlY-Lm76cwOxvPPnxMhoYIiaOqZZXYTIfcCB987uuM8F9wFOsCK-20yAojDbksvAw2lT6zVkvf6YZalzq0Ep_BXtM29SHEFZrCC2PT1AlZpGi84gNiFww6Kao0gnfrOJVuEAvnnhWXJYEGDmm5CWkEbzamP3qFjNuMTjjYGwMWte4GKNXlkOryX6mO4PU6VSUtAv6yYZu6vV6WQiqda5bai-B5n7rNoxAlFaFGR6B2krrjy-6dZvGtE9qm6knTexz9D-dfwAPBdJg0SzJ8CXurq-v6Fdx3P1eL5dUI7qq56q56BPdOxpPpl1E3o-l6_mdMY9NP59OvfwFsjvta |
| linkProvider | Directory of Open Access Journals |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Biosignal+Compression+Toolbox+for+Digital+Biomarker+Discovery&rft.jtitle=Sensors+%28Basel%2C+Switzerland%29&rft.au=Bent%2C+Brinnae&rft.au=Lu%2C+Baiying&rft.au=Kim%2C+Juseong&rft.au=Dunn%2C+Jessilyn+P&rft.date=2021-01-13&rft.issn=1424-8220&rft.eissn=1424-8220&rft.volume=21&rft.issue=2&rft_id=info:doi/10.3390%2Fs21020516&rft.externalDBID=NO_FULL_TEXT |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1424-8220&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1424-8220&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1424-8220&client=summon |